Remote sensing to derive calibrated power measurements

ABSTRACT

Described herein are methods and systems for remote sensing to derive a calibrated power measurement for a power distribution point. Magnetic field sensors of a sensor module sense a magnetic field emitted by the power distribution point. A first processor generates an uncalibrated power measurement for each magnetic field sensor, the uncalibrated power measurement derived from the magnetic field sensed by the magnetic field sensor and a voltage carried by the power distribution point. A second processor determines a response of each magnetic field sensor to a known power load being drawn through the power distribution point. The second processor derives a transfer function using the response of each magnetic field sensor to the known power load. The second processor applies the transfer function to the uncalibrated power measurement for each magnetic field sensor to generate the calibrated power measurement for the power distribution point.

RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationNo. 62/232,278, filed on Sep. 24, 2015, the entirety of which isincorporated herein by reference.

TECHNICAL FIELD

The subject matter of the application relates generally to remotesensing to derive calibrated power measurements.

BACKGROUND

Currently, measuring power usage loads and fluctuations for residentialand commercial environments is accomplished via Current Transformer (CT)clamp technology. Products such as smart meters and other energymonitoring products such as the TED 5000 series available from Energy,Inc. of Charleston, S.C., the Neurio Sensor available from NeurioTechnology, Inc. of Vancouver, BC, Canada and the EHEM1 Home ElectricityMonitor available from Eyedro Green Solutions, Inc. of Kitchener, ON,Canada utilize CT technology because it is relatively straightforward todiscern power usage, as the magnetic fields from a single wire which aCT clamp surrounds have a simple physical relationship to the electricalcurrent present in the wire. Such products typically determine the powerby multiplying the instantaneous current sensed by each CT clamp by thetime-varying voltage. By integrating this instantaneous power over atime period such as one second, the total power used during that onesecond time period can be calculated.

However, the installation of CT clamps is difficult and dangerousbecause the procedure requires removing of the outside protective panelof the electrical breaker box and further requires the installer to comein close contact with high-powered electrical lines that can causepersonal physical harm or even death. This sort of installation is notpractical for a homeowner to complete; most homeowners would not becomfortable performing the installation and instead opt to hire anelectrical contractor to perform the work at a significant expense.Also, CT clamp installations take a significant amount of time due tothe labor involved in removing fuse box covers, carefully connecting CTclamps and associated wires. Even an installation by a professionalelectrician can take thirty or more minutes.

SUMMARY

Therefore, what is needed is a method and system for measuring usage andfluctuations of power flowing through a power distribution point usingremote sensing technology that does not require complicated anddangerous installation of components, such as CT clamps, into theinterior of an electrical breaker box. The techniques described hereinprovide for a greatly simplified installation of a power measurementsystem by positioning a sensor module, equipped with magnetic fieldsensors, in proximity to a power distribution point. As an example,installations of the power measurement system described herein can bedone by any person, even persons with no experience in working withelectricity, and the installation can be complete in a short amount oftime, typically less than ten minutes.

In the context of this invention, a power distribution point is anypoint in a power distribution system that (i) receives electrical powerflowing from a power generation source (e.g., as operated by a utilityprovider, as provided from a solar panel array coupled to the powerdistribution point) and (ii) distributes the received electrical powerto one or more distribution channels. In one embodiment, a powerdistribution point is an electrical breaker box as installed at aresidence or business facility. An example of a power distribution pointas a residential electrical breaker box 100 is shown in FIG. 1A. In thisexample, the electrical breaker panel receives electrical power from autility provider via a service entrance cable 102 (i.e., a large cablethat usually enters at the top of an electrical breaker box and thattypically comprises three wires—a phase one wire 104, a phase two wire106, and a neutral wire 108) and distributes the electrical power tobranch circuits 110 that service different areas of the building. Thephase one wire 104 and the phase two wire 106 typically carry asinusoidal varying voltage of 120 volts. This is a standard two-phasewiring scheme and features electrical currents from each wire 104, 106that are flowing in opposite directions and voltages that are 180degrees out of phase. Note that the designations ‘phase one’ and ‘phasetwo’ for the phase wires are used herein for reference purposes todifferentiate one phase versus another. It should be appreciated that insome embodiments, there may be one, two, or three phases. For example, aresidential environment in Europe may have one phase, a residentialenvironment in the U.S. may have two phases, and a commercialenvironment in the U.S. may have three phases.

Other examples of power distribution points with which the inventiondescribed herein can be used include, but are not limited to, devicesthat are upstream from the electrical breaker box (i.e., closer to theutility provider) and/or downstream from the electrical breaker box(i.e., further from the utility provider). Such devices can includeother electrical panels that are coupled to the electrical breaker boxin the building (e.g., receptacles for charging electronic vehicles,solar panel electricity transfer systems), smart meters, generators,upstream devices at various nodes in the utility provider's electricaldistribution system, and so forth. It should also be appreciated that insome embodiments, a power distribution point can be a point on a wire orcable that is part of the power distribution system. For example, theportion of the service entrance cable that connects, e.g., a utilitymeter and an electrical breaker box can be considered a powerdistribution point because power is flowing through the wires of theservice entrance cable.

FIG. 1B is a diagram of a power distribution system that depictsexemplary locations for the sensor module described herein to bepositioned in relation to various power distribution points. As shown inFIG. 1B, a sensor module SM1 is positioned on a line between a utilitytransformer and a smart meter (e.g., as located at a residence). Sensormodule SM2 is positioned on the exterior box of the smart meter. Sensormodule SM3 is positioned on a wall in proximity to the electricalbreaker box that houses the main electrical panel of the residence(e.g., at or near the location that the service entrance cable entersthe main panel). Sensor module SM4 is positioned on the exterior of theelectrical breaker box. Sensor module SM5 is positioned on a linebetween the electrical breaker box and a sub electrical panel (e.g., adedicated electrical panel elsewhere in the residence). Sensor moduleSM6 is positioned on the exterior of the sub panel box. Sensor moduleSM7 is positioned on a line (or on a transfer apparatus) that couplessolar panels to the electrical breaker box.

Furthermore, in contrast to the CT clamp technology described above, thesystem and method described herein provides the advantage of sensingmore complex magnetic fields as produced by the power flowing throughthe power distribution point, such as the magnetic fields produced fromthe wires 104, 106 in the service entrance cable as well as the magneticfields produced by the power flowing through the individual branchcircuits 110 that, e.g., extend from the power distribution point tovarious locations in the building. The system and method alsoadvantageously use the data sensed via the magnetic field sensors toidentify individual branch circuits that are coupled to a powerdistribution point and further to identify and monitor specific devices(e.g., appliances, electronics, and the like) on each branch circuit.

Generally, the system and method described herein uses one or moremagnetic field sensors in a sensor module to sense the magnetic fieldemitted by the power distribution point as power flows through the powerdistribution point, including the branch circuits connected to the powerdistribution point. In some embodiments, the sensor module is positionedat different locations in proximity to the power distribution point. Inthe example of an electrical breaker box, the sensor module can beaffixed to the exterior of the electrical breaker box, on or near theservice line entrance cable, and/or on a wall in proximity to theelectrical breaker box. It should be appreciated that the abovelocations are merely examples, and the sensor module can be placedanywhere in proximity to the power distribution point where the magneticfields from the power flowing through the power distribution point canbe sensed. In addition, the techniques described herein do not generallyrequire a specific orientation of the magnetic field sensors to eachother and/or the power distribution point.

The invention, in one aspect, features a method for remote sensing toderive a calibrated power measurement for a power distribution point.One or more magnetic field sensors of a sensor module sense a magneticfield emitted by the power distribution point, where the sensor moduleis positioned in proximity to the power distribution point. A voltagesensor coupled to the sensor module senses a voltage carried in thepower distribution point. A first processor coupled to the sensor modulegenerates an uncalibrated power measurement for each magnetic fieldsensor, the uncalibrated power measurement derived from the magneticfield sensed by the magnetic field sensor and the voltage sensed by thevoltage sensor. A second processor coupled to the sensor moduledetermines a response of each magnetic field sensor to a known powerload being drawn through the power distribution point. The secondprocessor derives a transfer function using the response of eachmagnetic field sensor to the known power load. The second processorapplies the transfer function to the uncalibrated power measurement foreach magnetic field sensor to generate the calibrated power measurementfor the power distribution point.

The invention, in another aspect, features a system for remote sensingto derive a calibrated power measurement for a power distribution point.The system comprises a sensor module with one or more magnetic fieldsensors positioned in proximity to the power distribution point. Themagnetic field sensors are configured to sense a magnetic field emittedby the power distribution point. The system further comprises a voltagesensor coupled to the sensor module. The voltage sensor is configured tosense a voltage carried in the power distribution point. The systemfurther comprises a first processor coupled to the sensor module. Thefirst processor is configured to generate an uncalibrated powermeasurement for each magnetic field sensor. The uncalibrated powermeasurement is derived from the magnetic field sensed by the magneticfield sensor and the voltage sensed by the voltage sensor. The systemfurther comprises a second processor coupled to the sensor module. Thesecond processor is configured to determine a response of each magneticfield sensor to a known power load being drawn through the powerdistribution point, derive a transfer function using the response ofeach magnetic field sensor to the known power load, and apply thetransfer function to the uncalibrated power measurement for eachmagnetic field sensor to generate the calibrated power measurement forthe power distribution point.

The invention, in another aspect, features a method of remote sensing toderive a calibrated power measurement for a power distribution point.One or more magnetic field sensors of a sensor module sense a magneticfield emitted by the power distribution point, where the sensor moduleis positioned in proximity to the power distribution point. A firstprocessor coupled to the sensor module generates an uncalibrated currentmeasurement for each magnetic field sensor. The uncalibrated currentmeasurement is derived from the magnetic field sensed by the magneticfield sensor. A second processor coupled to the sensor module determinesa response of each magnetic field sensor to a known current load beingdrawn through the power distribution point. The second processor derivesa transfer function using the response of each magnetic field sensor tothe known current load. The second processor applies the transferfunction to the uncalibrated current measurement for each magnetic fieldsensor and a voltage carried by the power distribution point to generatethe calibrated power measurement for the power distribution point.

The invention, in another aspect, features a system for remote sensingto derive a calibrated power measurement for a power distribution point.The system comprises a sensor module with a sensor module with one ormore magnetic field sensors positioned in proximity to the powerdistribution point. The magnetic field sensors are configured to sense amagnetic field emitted by the power distribution point. The systemfurther comprises a first processor coupled to the sensor module, thefirst processor configured to generate an uncalibrated currentmeasurement for each magnetic field sensor. The uncalibrated currentmeasurement is derived from the magnetic field sensed by the magneticfield sensor. The system further comprises a second processor coupled tothe sensor module. The second processor is configured to determine aresponse of each magnetic field sensor to a known current load beingdrawn through the power distribution point; derive a transfer functionusing the response of each magnetic field sensor to the known currentload; and apply the transfer function to the uncalibrated currentmeasurement for each magnetic field sensor and a voltage carried by thepower distribution point to generate the calibrated power measurementfor the power distribution point.

Any of the above aspects can include one or more of the followingfeatures. In some embodiments, the power distribution point is anelectrical breaker box. In some embodiments, the sensor module isaffixed to the exterior of the electrical breaker box. In someembodiments, the voltage sensor is coupled to a power supplyelectrically connected to the power distribution point. In someembodiments, a calibrator circuit coupled to the power distributionpoint is activated to draw the known power load through the powerdistribution point. In some embodiments, the calibrator circuit is aresistor.

In some embodiments, deriving a transfer function comprises deriving aseries of coefficients each associated with the uncalibrated powermeasurement for a magnetic field sensor. In some embodiments, derivingthe series of coefficients comprises determining a quasi-power for eachof the magnetic field sensors based upon the uncalibrated powermeasurement for a magnetic field sensor. In some embodiments,determining the quasi-power comprises integrating for each magneticfield sensor the uncalibrated power measurement in time to determine acurrent (I) measured by the magnetic field sensor; multiplying thecurrent by a voltage from the voltage sensor (I·V); and integrating I·Vover one cycle.

In some embodiments, the second processor is located in a servercomputing device coupled to the sensor module. In some embodiments, thefirst processor transmits the uncalibrated power measurement for eachmagnetic field sensor to the server computing device. In someembodiments, the calibrated power measurement for the power distributionpoint is transmitted to a server computing device.

In some embodiments, the magnetic field sensors are arranged in apredetermined geometric configuration within a housing of the sensormodule. In some embodiments, the magnetic field sensors are arranged atdifferent orientations with respect to each other.

In some embodiments, a server computing device configured to collect aset of calibrated power measurements for each of a plurality of powerdistribution points distributed across a predefined geographical area,and determine one or more power signatures that are common across atleast a plurality of the sets of calibrated power measurements. In someembodiments, the one or more power signatures correspond to a failure ofequipment in a power generation system that is coupled to the pluralityof power distribution points.

The invention, in another aspect, features a method of identifyingindividual branch circuits coupled to a power distribution point. Aplurality of magnetic field sensors of a sensor module senses a magneticfield emitted by each of a plurality of branch circuits coupled to thepower distribution point, where the sensor module is positioned inproximity to the power distribution point. A processor coupled to thesensor module determines a response of each magnetic field sensor toeach of a plurality of changes in power associated with at least one ofthe plurality of branch circuits. The processor positions the responsesof the magnetic field sensors to each change in power on a point in amultidimensional space, where each dimension of the multidimensionalspace corresponds to a magnetic field sensor. The processor identifiesclusters of the points in the multidimensional space, each clusterrepresenting a different branch circuit and having a different vectordirection.

The invention, in another aspect, features a system for identifyingindividual branch circuits coupled to a power distribution point. Thesystem comprises a sensor module positioned in proximity to the powerdistribution point and having a plurality of magnetic field sensorsconfigured to sense a magnetic field emitted by each of a plurality ofbranch circuits coupled to the power distribution point. The systemfurther comprises a processor coupled to the sensor module, theprocessor configured to determine a response of each magnetic fieldsensor to each of a plurality of changes in power associated with atleast one of the plurality of branch circuits. The processor is furtherconfigured to position the responses of the magnetic field sensors toeach change in power on a point in a multidimensional space, where eachdimension of the multidimensional space corresponds to a magnetic fieldsensor. The processor is further configured to identify clusters of thepoints in the multidimensional space, each cluster representing adifferent branch circuit and having a different vector direction.

Other aspects and advantages of the invention will become apparent fromthe following detailed description, taken in conjunction with theaccompanying drawings, illustrating the principles of the invention byway of example only.

BRIEF DESCRIPTION OF THE DRAWINGS

The advantages of the invention described above, together with furtheradvantages, may be better understood by referring to the followingdescription taken in conjunction with the accompanying drawings. Thedrawings are not necessarily to scale, emphasis instead generally beingplaced upon illustrating the principles of the invention.

FIG. 1A is a diagram of an electrical breaker box.

FIG. 1B is a diagram of a power distribution system that depictsexemplary locations for the sensor module to be positioned in relationto various power distribution points.

FIG. 2 is a block diagram of a system for remote sensing to derivecalibrated power measurements for a power distribution point.

FIG. 3 is a flow diagram of a method for remote sensing to derivecalibrated power measurements for a power distribution point.

FIG. 4 is a diagram of a graph showing waveforms for instantaneouspower, current (dI/dt integrated), and voltage.

FIG. 5 is a diagram of a sensor module with three magnetic field sensorspositioned in proximity to the service entrance cable of a powerdistribution point.

FIG. 6 is an exemplary plot of the linear relationship between powerchanges sensed by magnetic field sensors and voltage changes.

FIGS. 7A-7C are exemplary plots of the magnetic field changes detectedby each of magnetic field sensors A, B, and C as plotted against eachother (e.g., A vs. B, B vs. C, A vs. C).

FIG. 8 is a diagram of different waveforms associated with measuringdI/dt on the power distribution point.

FIG. 9 is a diagram of waveforms showing voltage at three differenttimes with different types of appliances running.

FIG. 10 is a flow diagram of a method of identifying individual branchcircuits coupled to a power distribution point.

FIG. 11 is an exemplary plot of the linear relationship between powerchanges sensed by magnetic field sensors and voltage changes, includingclusters and vector directions.

FIG. 12 is a diagram of graphs to show how a waveform of power output isanalyzed to produce a change detection.

FIGS. 13 and 14 are diagrams of screenshots of an exemplary reportingdashboard user interface.

FIG. 15 is a diagram of a screenshot of an exemplary user interface formapping a location in the home to a circuit.

FIG. 16 is a diagram of an exemplary dot graph depicting the power usagefor devices on certain branch circuits.

DETAILED DESCRIPTION

FIG. 2 is a block diagram of a system 200 for remote sensing to derive acalibrated power measurement for a power distribution point. The system200 includes a service entrance cable 202 that enters a powerdistribution point 204 (e.g., an electrical breaker box) that transfersthe power received from the service entrance cable to, e.g., branchcircuits 205 that feed the power to appliances and outlets, such as onewould find in a residential environment, or other building or facility.

The system 200 further includes a sensor module 206 positioned inproximity to the power distribution point 204. In the example shown inFIG. 2, the sensor module 206 is affixed to the exterior of the powerdistribution point 204. However, as set forth above, it should beappreciated that the sensor module in some embodiments is positioned invarious locations that are in proximity to the power distribution point204, such as on or near the service entrance cable 202 or on a wall nearthe power distribution point 204.

The sensor module 206 includes one or more magnetic field sensors.Exemplary magnetic field sensors include, but are not limited to: HallEffect sensors, magneto resistive sensors, or magnetic coil pickups. Asshown in FIG. 2, the sensor module includes three sensors (i.e., thegray circles within the sensor module 206) that sense the magneticfields emitted from the wires of the service entrance cable 202 thatconnect to the power distribution point 204 and the wires that comprisethe branch circuits 205. In some embodiments, the sensors are orientedwithin the sensor module to detect the magnetic fields from differentdistances and/or angles around the power distribution point 204. Forexample, one sensor or group of sensors can be positioned within thesensor module 206 such that the faces of each of the sensor(s) are notoriented at a 90-degree angle to the service entrance cable 202/powerdistribution point 204/branch circuits 205, but instead are oriented atdifferent angle(s). Also, in some embodiments, the sensor module 206includes a processor that can generate an uncalibrated power measurementfor each magnetic field sensor, as will be described below. Asreferenced above, it should be appreciated that in some embodiments thesensor module 206 includes a single sensor and in other embodiments, thesensor module 206 includes a plurality of sensors.

As shown in FIG. 2, the sensor module 206 is coupled to a cable (e.g., anetwork cable) that connects the sensor module 206 to a data measurementand processing device 208. However, it should be appreciated that otherconfigurations of the elements shown in FIG. 2 can be implementedwithout departing from the scope of invention. For example, althoughFIG. 2 shows a single sensor module that is communicatively coupled to aseparate data measurement and processing device 208, it should beappreciated that the sensor module 206 and the data measurement andprocessing device 208 can be located within the same physicalhousing—thereby comprising a single device. In another example, thesensor module 206 and the data measurement and processing device 208 canbe equipped with wireless networking circuitry (e.g., WiFi antenna) thatenables the respective devices 206, 208 to communicate as describedherein. In some embodiments, the sensor module 206 can be based upon theSTM32F205 ARM Cortex MS processor by ST Micro and the Broadcom BCM43362WiFi chip for communications, which provides 12-bit A/D and wirelesscommunications in a single device.

As can be expected, the branch circuits 205 that receive power from thepower distribution point 204 and distribute the power also emit magneticfields that can be detected by the sensor module 206. In some instances,the magnetic fields emitted by the branch circuits 205 may be consideredas ‘noise’ because these fields can alter the readings obtained by thesensors of the magnetic fields emitted by the power distribution point204 and/or the service entrance cable 202. However, the magnetic fieldemitted by each branch circuit 205 can also be considered as a separatesignal to be captured by the magnetic field sensors of the sensor module206, and analyzed by the data measurement and processing device 208. Itshould be appreciated that while FIG. 2 depicts three sensors within thesensor module 206, the module can include various other numbers ofsensors (e.g., four, six, ten, twelve) that detect the magnetic fieldsfrom the power distribution point 204 and also to detect the magneticfields from the branch circuits 205. In some embodiments, the magneticfield sensors of the sensor module 206 can be arranged in a particulargeometric configuration in relation to each other.

Further, in some embodiments, the sensor module 206 can also include orbe coupled to a radio frequency (RF) antenna configured to measure atime-varying electric field produced by the time-varying voltage (dV/dt)of the power distribution point 204. Because the RF antenna senses thetime-varying component of the electric field, and hence the time-varyingcomponent of the voltage on the power distribution point 204, the systemis able to detect very fast changing voltages that may be caused by avariety of circumstances, some potentially dangerous, such as surgesintroduced from the utility distribution system, large appliancesturning on and off, electrical shorts and/or sparks in the localelectrical system, poor power quality, and so forth. The system can beconfigured to detect these changing voltages and generate reports and/oralerts to warn end users about possible dangers existing in theelectrical system—as will be described in greater detail below.

Continuing with FIG. 2, the data measurement and processing device 208receives magnetic field measurement data from the magnetic field sensorsof the sensor module 206 and, in some embodiments, processes themeasurement data to determine both uncalibrated power measurements andcalibrated power measurements. The data measurement and processingdevice 208 comprises computing hardware such as one or more processors,memory, a communications module (e.g., WiFi circuitry), a calibrationcircuit 209, and other signal processing circuitry to perform the datacollection, calibration, and power measurement processes describedherein. In one embodiment, the data measurement and processing device208 comprises two processors to receive and process the data receivedfrom the sensor module 206; a first one of the processors is configuredto generate an uncalibrated power measurement for each magnetic fieldsensor and a second one of the processors is configured to generate acalibrated power measurement for the power distribution point, as willbe described below. In other embodiments, the processing described inthis paragraph can be executed by a single processor located in eitherthe sensor module 206 or the data measurement and processing device 208.In still other embodiments, the first processor and the second processorcan be located in any of the devices described herein—such as the sensormodule 206, the data measurement and processing device 208, and/or theserver computing device 214.

The data measurement and processing device 208 also includes a powersupply 211 that is connected to a receptacle (e.g., a power outlet) viaa 120 VAC plug 210 (in some embodiments, the plug can be 220V). In oneembodiment, the device 208 is based upon the STM32F205 ARM Cortex MSprocessor by ST Micro and the Broadcom BCM43362 WiFi chip forcommunications, which provides 12-bit A/D and wireless communications ina single device. The data measurement and processing device 208, or insome cases the power supply 211 itself, further includes a voltagesensor 212 that is capable of measuring voltage directly on the powerline within the power supply 211.

The data measurement and processing device 208 is further coupled to acommunications network 213 that enables the device 208 to communicatewith a server computing device 214 that, in some embodiments, may be ata different location than the data measurement and processing device208. For example, the device 208 can communicate via the Internet (e.g.,via a wireless connection to a router or other apparatus installedlocally) to transmit power measurement data to the server computingdevice 214. In some embodiments, the server computing device 214 canstore the power measurement data and provide other functions or serviceswith respect to the power measurement data. As mentioned above, thesensor module 206 and/or the data measurement and processing device 208can transmit data to the server computing device 214—such as sending theserver computing device 214 uncalibrated power measurement data so thatthe server computing device 214 can generate the calibrated powermeasurements. In another example, the server computing device 214 canenable a user to access power measurement data for his or her residence(e.g., via a browser interface) and view reports, data summaries,alerts, and other types of information that relate to the powermeasurement data being captured and processed by the system 200, as willbe described in greater detail below.

FIG. 3 is a flow diagram of a method 300 for remote sensing to derive acalibrated power measurement for a power distribution point, using thesystem 200 of FIG. 2. One or more magnetic field sensors of the sensormodule 206 senses (302) a magnetic field emitted by the powerdistribution point 204. As mentioned above, and as shown in FIG. 2, thesensor module 206 is positioned in proximity to the power distributionpoint 204. Each magnetic field sensor generates an output voltage thatis proportional to the magnetic fields detected by the sensor, where themagnetic field is proportional to the changing current flowing througheach of the wires (dI/dt). As noted above, in some embodiments themagnetic field sensor is a Hall Effect sensor, which measures themagnetic field proportional to the current (I) flowing through thewires, not the changing current.

A voltage sensor (e.g., voltage sensor 212) senses (304) a voltagecarried in the power distribution point 204. A first processor coupledto the sensor module 206 generates (306) an uncalibrated powermeasurement for each magnetic field sensor. The first processor deriveseach uncalibrated power measurement from the magnetic field sensed bythe respective sensor and the voltage sensed by the voltage sensor. Forexample, because each magnetic field sensor produces an output relatedto dI/dt, the processor of the sensor device 206 (or in someembodiments, the data measurement and processing device 208) integratesthe output from the sensor to produce instantaneous current at the timeof the measurement. As noted above, it should be appreciated that HallEffect sensors measures the magnetic field proportional to I, which doesnot need to be integrated.

As one example, FIG. 4 depicts waveforms that correspond to theinstantaneous power 472, the current (dI/dt integrated) 474, and thevoltage 476. The first processor coupled to the sensor module 206multiplies the integrated current by the instantaneous voltage, and thefirst processor integrates the instantaneous power over a time period toproduce an uncalibrated power output. At this juncture, the firstprocessor determines an uncalibrated power output for each of themagnetic field sensors because the first processor does not havedetailed information about, e.g., how close each magnetic field sensoris to each of the wires in the power distribution point 204 and thusthere is not enough information at this point to determine an exactcurrent flow.

As can be appreciated, the magnetic field sensed by each sensor in thesensor module 206 is a linear combination of the magnetic field sensedfrom each wire in the power distribution point 204 (e.g., the phase onewire 104, the phase two wire 106, and the neutral wire 108 of theservice entrance cable 202, and/or each branch circuit 205). In asimplified example, FIG. 5 is a diagram of a sensor module 206 withthree magnetic field sensors A, B, and C that are positioned inproximity to the wires of the service entrance cable 202 in the powerdistribution point 204 (i.e., the phase one wire 104, the phase two wire106 and the neutral wire 108). Using this configuration as an example,the magnetic field from wire 104 is detected more strongly by sensor Athan sensors B or C because sensor A is closer to wire 104 than sensorsB and C. Similarly, the magnetic field from wire 106 is detected morestrongly by sensor B than sensors A or C because sensor B is closer towire 106 than sensors A and C.

As is known with respect to CT clamps, calibration of a CT clamp isrelatively simple because the measurement obtained by the CT clamp isdirectly proportional to the power flowing through the line to which theCT clamp is attached. However, in the system and method describedherein, calibration of the magnetic field sensors is more complicatedbecause the proportionality of the signal detected by the sensors to thepower flowing through the corresponding line is not known and must becalculated.

Turning back to FIG. 3, a second processor coupled to the sensor module206 (e.g., a processor located at the data measurement and processingdevice 208 and/or a processor located at the server computing device214) proceeds to calibrate the uncalibrated power measurement generatedby the first processor. The second processor determines (308) a responseof each magnetic field sensor to a known power load being drawn throughthe power distribution point 204. In one example, the second processoris coupled to a calibration circuit (e.g., calibration circuit 209 ofthe data measurement and processing device 208) capable of introducingresistive and/or reactive loads to the power distribution point 204. Itshould be appreciated that the calibration circuit can be coupled to thesystem 200 in a variety of configurations, such as having a separatecalibrator device that is plugged into, e.g., a branch circuit 205connected to the power distribution point 204. The second processor canbe programmed to activate the calibration circuit 209 at predeterminedtimes (e.g., turning on a reed switch) so that measurements receivedfrom the magnetic field sensors of the sensor module 206 at those timescan be evaluated to determine the impact that adding the known powerload has on the overall power measurements, and also to determine therelative impact that the known power load has on the wire(s) beingmonitored by each of the sensors.

In some embodiments, the amount of the known power load can bedetermined in a variety of ways, including but not limited to obtainingmeasurements from a smart plug coupled to a branch circuit 205 thatconnects to the power distribution point 204, obtaining measurementsfrom the power supply 211 of the data measurement and processing device208, or obtaining measurements from a smart meter coupled to the powerdistribution point 204.

The overall power load can also be determined by sensing voltage (e.g.,using the voltage sensor) and estimating power associated with voltagechanges (i.e., determining a relationship between the power flow andvoltage changes). FIG. 6 is an exemplary plot of the linear relationshipbetween power changes observed on Phase 1 and Phase 2 [of a home withtwo phase power] versus voltage changes as measured from the powersupply 211 of the data measurement and processing device 208. As shownin FIG. 6, line 602 shows the linear relationship between the power flowon Phase 1 and line 604 shows the linear relationship for Phase 2. Therelationship between power changes and voltage changes as seen on asingle circuit calibrated with a known load can be used to derive powerchanges for circuits which have not been, or are unable to be calibratedwith a known load. Although the voltage signal is somewhat noisy, byaggregating and clustering many voltage versus power changes over time amore exact relationship can be determined and thus improve the accuracyof the power usage at the power distribution point.

FIGS. 7A-7C each comprises an exemplary plot of the magnetic fieldchanges detected by each of the magnetic field sensors A, B, and C asplotted against each other (e.g., A vs. B, B vs. C, A vs. C). As shownin FIGS. 7A-7C, each of the plots comprises an x-axis and a y-axis thatcorrespond to the respective sensors being plotted. The points on eachline represent detection of a change in the known current that is drawnthrough the power distribution point 204 by the calibration circuit 209.

As shown in FIGS. 7A-7C, the plots comprise two-dimensional data for twomagnetic field sensors for ease of representation and illustration.However, it should be appreciated that the second processor plots themagnetic fields detected by each of the sensors against each of theother sensors, regardless of how many sensors are implemented in thesensor module 206. For example, if the sensor module 206 includes eightsensors A through H, the second processor plots the magnetic fieldsdetected by sensor A against each of the other sensors B through H, andso forth for each sensor—resulting in eight dimensions of data, andcorrespondingly, an eight-dimensional space for plotting the magneticfield changes. As such, computer processing is required to generate thishigh-dimensional data—which cannot be done without advanced computerprocessing algorithms and techniques.

Using a data set with high dimensionality enables the second processorto capture and understand variances in each of the respective wires(e.g., wires of service entrance cable 202, wires of branch circuits205) of the power distribution point 204 because the measurement datafor each of the branch circuit wires is almost certain to be differentenough to be distinctive from the other branch circuit wires. As such,the second processor has the capability to tell each of the wires aparteven if there is noise, and can detect subtle changes in the powerprofile on each specific branch circuit—which provides several usefuladvantages as will be detailed later in the specification.

Turning back to FIG. 3, the second processor derives (310) a transferfunction using the response of each magnetic field sensor to the knownpower load and then applies (312) the transfer function to theuncalibrated power measurement for each magnetic field sensor togenerate a calibrated power measurement for the power distribution point204. As mentioned previously, an important feature of the methods andsystems described herein is to generate a calibrated power measurementfor the power distribution point 204 using the uncalibrated powermeasurements obtained from each magnetic field sensor of the sensormodule 206. Generally, the overall power P flowing through the powerdistribution point 204 can be represented by the following equation:

P=C ₀ ·P _(A) +C ₁ ·P _(B) +C ₂ ·P _(C) + . . . +C _(i) ·P _(n)

where:

-   -   Pn=output of sensor n;    -   C_(i)=coefficient.

The coefficients associated with each magnetic field sensor output areinfluenced by three factors:

-   -   1) geometry of the sensor relative to the wires, 1/r, where r is        the distance from the sensor to the wire being sensed;    -   2) size of the magnetic loop formed by the circuit; and    -   3) orientation of the sensor, which falls within a range of −1        to 1.

All three values, 1/r, the size of the loop, and the orientation of thesensor are static values that do not change.

To determine what the coefficients are, the second processor performsthe following steps:

-   -   1) A “quasi-power” (Q) for each magnetic field sensor (e.g., A        through L) that each detects magnetic fields from n wires can be        represented by the following equation:

$\frac{d\; Q}{d\; t_{A}} = {{{C_{1A} \cdot \frac{d\; I}{dt}}{wire}_{1}} + {{C_{2\; A} \cdot \frac{d\; I}{dt}}{wire}_{2}} + \ldots + {{C_{nA} \cdot \frac{d\; I}{dt}}{wire}_{n}}}$…$\frac{d\; Q}{{Dt}_{L}} = {{{C_{1L} \cdot \frac{d\; I}{dt}}{wire}_{1}} + {{C_{2\; L} \cdot \frac{d\; I}{dt}}{wire}_{2}} + \ldots + {{C_{nL} \cdot \frac{d\; I}{dt}}{wire}_{n}}}$

-   -   2) Each of the above equations is continuously integrated over        time as shown in the following example for sensor A:

$Q_{A} = {{C_{1A} \cdot {\int{\frac{d\; I}{dt}{wire}_{1}}}} + {C_{2A} \cdot {\int{\frac{d\; I}{dt}{wire}_{1}}}} + \ldots + {C_{nA} \cdot {\int{\frac{d\; I}{dt}{wire}_{n}}}}}$

to result in the following equation where I(t) represents the current:

Q _(A) =C _(1A) ·I(t)wire₁ +C _(2A) ·I(t)wire₂ + . . . +C _(nA)·I(t)wire_(n)

-   -   3) Both sides of the equation are then multiplied by the voltage        V(t) to determine an instantaneous quasi-power:

Q _(A) ·V(t)=C _(1A)·(I(t)·V(t))wire₁ +C _(2A)·(I(t)·V(t))wire₂ + . . .+C _(nA)·(I(t)·V(t))wire_(n)

-   -   4) The equation is then integrated over one or more 60 Hz power        cycles to determine an average quasi-power (P) detected by the        sensor:

P _(QA) =C _(1A)·∫(I(t)·V(t))wire₁ +C _(2A)·∫(I(t)·V(t))wire₂ + . . . +C_(nA)·∫(I(t)·V(t))wire_(n)

or

P _(QA) =C _(1A) ·P ₁wire₁ +C _(2A) ·P ₂wire₂ + . . . +C _(nA) ·P_(n)wire_(n)

P _(QL) =C _(1L) ·P ₁wire₁ +C _(2L) ·P ₂wire₂ + . . . +C _(nL) ·P_(n)wire_(n)

The equation above calculates quasi-power at each magnetic field sensoras a function of power flow in each circuit. Inverting the matrix C(C_(1A) to C_(nL)) with the response of the sensors to the knowncalibration load(s) gives an equation of power flow in each circuit as afunction of quasi-power at each sensor. The final calibrated power isthe signed sum of the power flow in each circuit.

As can be appreciated, power measurement systems that utilize CT clampsdo not perform any of the above calculations because the CT clampsmeasure current directly on the wire to which they are attached. As aresult, those systems do not need to calculate calibration coefficientsas outlined above.

In some embodiments, the calibration circuit 209 can be configured todraw a small known load through the power distribution point 204. In oneexample, the calibration circuit 209 is a resistor drawing a current forvarious numbers of 60 Hz power cycles. Also, in some embodiments, thesecond processor uses matched field processing in order to determine thecalibrated power measurement. For example, the calibration circuit 209can be configured to draw a known load through the power distributionpoint 204 every minute at a one-second interval. Because the secondprocessor is configured to understand the time at which the known loadis drawn through the power distribution point 204, the second processoradds the individual measurements taken each second together to determinea calibrated power measurement for the power distribution point 204. Inanother example, the calibration circuit 209 can draw a small known loadon a specific cycle of voltage. Because the drawn power is occurring ata specific voltage cycle, the second processor knows which phase thecalibration circuit 209 is located on in relation to the phase of thevoltage measurement by the second processor.

In some cases, the sensor module 206 can be ‘underdetermined’ for thepower distribution point 204 by including fewer magnetic field sensorsthan the number of wires being monitored. However, the systems andmethods described herein can be adapted to produce calibrated powermeasurement data for all of the wires, even when the power distributionpoint 204 has more wires drawing power than the sensor module 206 hassensors. In this example, the second processor can store the data (e.g.,measurements and coefficients) for a known wire (e.g., branch circuit)in an auxiliary memory location that is not currently being utilized toperform the continuous calculations described above. The secondprocessor then recalibrates the power measurement data for the remainingbranch circuits, including a newly-turned on branch circuit. Forexample, if branch circuits one through seven are in use and then brancheight is turned on, the second processor stores data for branch circuitone in the auxiliary memory and recalibrates the power measurement datafor branch circuits two through eight (as described above). If a changesubsequently occurs on branch circuit one, the second processor candetermine the number of watts associated with the change and add thatvalue to the power measurement across branch circuits two through eightthat are currently being monitored.

As provided above, the RF antenna of the system 200 can be configured tomeasure a time-varying electric field produced by the time-varyingvoltage of the system 200, and thus detect very fast changing voltagesthat may be caused by a variety of circumstances. For example, thewaveforms in FIG. 8 show the dI/dt output voltage at three differenttimes with different types of appliances running. Waveforms 852 and 853correspond to small appliances running with only a small amount ofpower. Note that there are differences in the waveforms that point tothe uniqueness of these waveforms for identifying devices running atthat time. Waveform 854, in contrast, corresponds to an air conditioningunit running. Note the high level of detail and various rates of changeof each dI/dt graph that correspond to unique signatures of the relatedappliances. The measurements can be tracked and aggregated to identifyproblems or dangerous conditions before they cause significant damage orthreat (i.e., a spark that, if left unresolved, could start a housefire). In order to detect these details and rates of change (that may bequite small), the first processor (e.g., embedded in the sensor module206) can be configured to sample data at a high rate (e.g., 100 MHz).

FIG. 9 is a diagram of different waveforms associated with measuringvoltage on the power distribution point 204, where waveform 902represents a measurement of voltage on the line directly (e.g., bydirectly reading from the 120 Volt power line through a resistor dividercircuit), and waveforms 904 and 906 represent measurements oftime-varying voltage by the RF antenna of the system 200. As shown inFIG. 9, waveform 902 is smooth and continuous—such that very fast and/orvery small changes in voltage are not readily visible. In contrast, thewaveforms 904, 906 that are captured by the RF antenna measuringtime-varying voltage (dV/dt) contain variances (i.e., jagged lines,peaks, changes in direction) that represent very fast and/or very smallchanges in voltage. Any of these variances can represent potentialdangerous conditions in the electrical system as explained above.Because the system 200 can detect these variances, the measurements canbe tracked and aggregated to identify problems or dangerous conditionsbefore they cause significant damage or threat (i.e., a spark that, ifleft unresolved, could start a house fire).

Another important advantage provided by the systems and methodsdescribed herein is the ability to identify individual branch circuits205 coupled to the power distribution point 204 and monitor the poweractivity on the individual branch circuits—including identification ofspecific devices (e.g., appliances, light fixtures, and so forth) thatare coupled to certain branch circuits. For example, appliances in ahouse may have different resistive and reactive loads (inductive orcapacitive). Also, many appliances such as those with switching powersupplies are designed to pull current at the top of the voltage cycle.Therefore, the power output of the power distribution point 204(determined by the systems and methods described above) can be furtheranalyzed to understand phase shifts between the supplied voltage and thecurrent delivered to the electrical system that are indicative ofappliances that turn on and off.

FIG. 10 is a flow diagram of a method of identifying individual branchcircuits coupled to a power distribution point, using the system 200 ofFIG. 2. The magnetic field sensors of sensor module 206 sense (1002) amagnetic field emitted by each of a plurality of branch circuits (e.g.,branch circuits 205). A processor (e.g., embedded in the sensor module206, the data measurement and processing module 208, and/or the servercomputing device 214) determines (1004) a response of each magneticfield sensor to each of a plurality of changes in power associated withat least one of the plurality of branch circuits 205. For example, asdevices such as appliances that are coupled to a certain branch circuit205 change state (i.e., the devices turn on/off), the correspondingchanges in power being drawn through the power distribution point 204via the branch circuit 205 are sensed by the magnetic field sensors. Theprocessor receives data associated with these power changes from each ofthe sensors.

The processor positions (1006) the responses of the magnetic fieldsensors to each change in power on a point in a multidimensional spaceand identifies (1008) clusters of points in the multidimensional space.As set forth above, if the sensor module 206 includes eight sensors Athrough H, the processor plots the magnetic fields detected by sensor Aagainst each of the other sensor B through H, and so forth for eachsensor—resulting in eight dimensions of data. Therefore, each dimensionin the multidimensional space corresponds to a certain magnetic fieldsensor. Due to the generally linear relationship between power changesdetected by one magnetic field sensor in relation to changes detected bya second magnetic field sensor, the interrelated responses of themagnetic field sensors are arranged in linear clusters along a vectordirection within the multidimensional space. For example, as shown inFIG. 11, the points are clustered along three distinct vector directions1102, 1104, 1106. The processor then identifies these clusters as eachrepresenting a different branch circuit. It should be appreciated thateven if the relationship is non-linear, the separate circuits are stilllikely to form separate clusters in the multidimensional space.

As mentioned above, using a data set with high dimensionality enables aview into power variances on each of the respective wires of the powerdistribution point 204 because the measurement data for each of thewires is almost certain to be different enough to be distinctive fromthe other wires. As such, the second processor has the capability totell each of the wires apart even if there is noise, and can detectsubtle changes in the power profile on each specific branch circuit.

Furthermore, the processor can determine an amplitude associated witheach change in power and group one or more points in themultidimensional space that are associated with a single branch circuitand that have a similar amplitude. For example, a device that is coupledto a specific branch circuit 205 draws the same amount of power throughthe power distribution point 204 via the branch circuit each time thedevice turns on. It can be assumed that this change in power from asingle device generally has the same amplitude. Therefore, the processorplots the points associated with the responses of the magnetic fieldsensors that the change in power at or close to the same location in themultidimensional space. The processor can then determine that a group ofpoints in a specific cluster (i.e., thereby being associated with aspecific branch circuit) that are close to each other in themultidimensional space correspond to the same device on that branchcircuit. Then, the processor identifies a state change of a devicecoupled to the branch circuit based upon the group of points and theamplitude. These processing techniques provide a significant advantagein the context of device power disaggregation because all devices thatare connected to the branch circuits of a particular power distributionsystem (e.g., an electrical breaker box in a home) are segregated firstby identification of the branch circuit to which they are connected. Asa result, the system provides a more efficient and simpler way toidentify and track devices and their power usage due to thissegregation. Existing technology such as CT clamps are deficient becausethey cannot accomplish segregation of devices by branch circuit.

FIG. 12 is a diagram of several graphs that show how the waveform of thepower output can be analyzed to produce a change detection where eachspike represents an appliance or other electrical device turning on oroff. As shown in FIG. 12, the top waveform 1202 represents the poweroutput of the power distribution point 204 for a particular period oftime, as determined by the power measurements taken and processed by thesensor module 206 and the data measurement and processing device 208.Currently, change detection techniques look for simple abrupt changes inthe power level to attempt to determine when devices are turning on andoff. As can be seen in graph 1204, this leads to a very noisyenvironment and makes it difficult to isolate specific appliances. Bycomputing real-time phase differences between the current and thevoltage, the system 200 can derive a phase surface graph 1206 that showsthe phase shifts which occur as the supplied voltage and currentdelivered change over time. As represented in FIG. 12, the spikesdepicted in graph 1204 generally line up with the phase shifts occurringin graph 1206 (see arrows 1210). Lastly, the device 208 can convert thephase surface graph into a change detection scan 1208, where the spikescorrespond to phase shifts (see arrows 1212) and represent, e.g., anappliance turning on or off. As a result, using this technique thesystem 200 can more easily isolate specific points in time whensignificant changes in power occur and represent them in a readilyunderstandable way—in order to understand power usage of appliances andidentify any potential issues.

Also, as set forth above, the data measurement and processing device 208can be coupled to a network 212 in order to communicate with a servercomputing device 214 that may be located remotely from the home orbusiness in which the electrical panel 204, sensor module 206 and device208 are installed. The server computing device 214 can receive powermeasurement data from the device 208 for storage (e.g., in a databasecoupled to the server 214) and further processing (e.g., calibration ofcircuits), and to provide related services—such as reporting andalerting functions. In one example, a homeowner can access the servercomputing device (e.g., a PC or mobile phone using an Internet browser)to review power measurement data for his or her home and receive relatedinformation, such as energy usage reports, safety alerts, notifications,and schedules. The server computing device 214 can be configured toprovide power measurement data and related information in real-time, sothat the homeowner has an up-to-date and accurate picture of power usagein the home. In some embodiments, the user can specify to the system 200when particular appliances or devices are on and/or off, and the system200 can correlate this user-provided information to the data collectedby the sensor module 206 and processed by the device 208 to detectspecific voltage/current/power consumption signatures associated withthe appliances and then use those signatures to identify the sameappliances during future analyses of the power measurement data. Also,the user interface can detect signatures for certain devices orappliances that may not be recognized or known to the system 200 inadvance, and display those to a user via the reporting function.

FIG. 13 is a diagram of screenshots of an exemplary reporting dashboarduser interface provided by the server computing device 214 of system200. As shown in FIG. 13, the dashboard user interface includes variousreports and tabulations associated with the power measurement beingperformed by the sensor module 206 and the data measurement andprocessing device 208. For example, the dashboard can include a reporton current energy usage 1302, including a graph 1304 showing activationand deactivation of certain appliances or power consumption devices inthe home (HVAC, refrigerator, kitchen lights, and so forth), and thecorresponding impact on energy usage. The current energy usage 1302report also includes a table 1306 showing the devices/appliances thatare using the most energy right now, along with a corresponding cost perhour to provide the energy being used. The dashboard can also include anenergy usage report 1308 that shows the current status of each device(i.e., red=off, green=on) along with the power usage and cost per hour,in an easy to review format.

Next, the dashboard can also include a chart 1310 that shows the energyconsumption of each device/appliance in the home throughout the day. Forexample, as shown in chart 1310, the main HVAC system in the home wasrunning from 7 am to 10 am, again from 12 pm to 6 pm, and again from 8pm to 12 am. In another example, the cable TV box was consuming powerconstantly throughout the day. The user interface depicted in FIG. 13provides a clear picture to the homeowner of the operation of devicesand appliances in the home, as well as their corresponding energyconsumption and cost per hour. Furthermore, any of the user interfacesin the dashboard can be augmented with the mapping data as describedabove, such that the dashboard can indicate that the cable TV box in aspecific upstairs bedroom (which is associated with a specific circuitin the home) was consuming power constantly throughout the day.

FIG. 14 is a diagram of additional screenshots of the exemplaryreporting dashboard user interface provided by the server computingdevice 214 of system 200. As shown in FIG. 14, the dashboard can includea home energy summary report 1402 with a chart 1404 showing overallenergy usage in the home during a certain time period (e.g., today). Thechart 1404 can be configured to show energy usage for other timeperiods, such as the last two days, last seven days, last month, andlast year. The dashboard can also include a safety notifications table1406 that lists common electrical hazards in the home along with astatus indicator to show the homeowner whether one of these hazardscould be present in the home, based upon the system's 200 detection andanalysis of power usage, as described in detail above.

As can be appreciated, the portable, plug-in calibrator described abovecan be used in conjunction with a mobile application (e.g., executing ona smartphone or other mobile computing device) in order to identifywhich circuits correspond to specific locations in the home. Forexample, a user can plug the portable calibrator into a receptacle in aparticular room of the home that is located on a certain branch circuit,and the calibrator can draw a known power load through the powerdistribution point 204. In one example, the magnetic field data sensedby the sensor module 206 and processed by the data measurement andprocessing device 208 can be sent to the server computing device 214 forcalibration as described previously. In addition, the mobile applicationcommunicates with the server computing device 214 to provide anotification to the user when the calibration is complete and to receiveinformation from the user regarding the location of the calibrator inthe home that can be used to map the location to the circuit beingcalibrated.

FIG. 15 is a screenshot of an exemplary user interface 1500 for mappinga location in the home to a branch circuit. As shown in FIG. 15, theuser interface 1500 includes an indicator 1502 (e.g., text and/or apercentage graphic) that calibration of the branch circuit to which thecalibrator has been connected is complete. The user interface 1500 alsoincludes a user input field 1504 that enables the user to enter, e.g.,the name of the room in which the calibrator is plugged in. The mobiledevice transmits the user input to the server computing device 214,which stores the user input in a mapping to the branch circuit data.Once the system 200 has generated this mapping data, subsequentnotification of potential hazards in the household electrical system caninclude the location data and thus be more Precise and actionable forthe user (e.g., a 100-watt device has turned on in the kitchen or apotential fire hazard is detected on the living room branch circuit).

FIG. 16 is a diagram of an exemplary dot graph depicting the power usagefor devices on certain branch circuits based upon the measurements andanalysis performed by the system 200 of FIG. 2 as described above. Asshown in FIG. 16, the graph at the top 1602 depicts the total powerflowing through the power distribution point (e.g., an electricalbreaker box) to which each of the branch circuits 1604 is connected overa period of time (from left to right).

Each of the rows associated with a particular branch circuit includes aseries of dots in varying colors (e.g., green & red, light gray & darkgray). Each of the dots correspond to a point in time when a devicecoupled to the corresponding branch circuit 1604 turned on or off. Forexample, a red dot indicates that a device turned off while a green dotrepresents when a device turned on. The size of a dot relates to theamount of power of the device. It should be noted that the branchcircuits 1604 are labeled (e.g., using the mobile application asdescribed above with respect to FIG. 15) according to their location,such that a user can easily and quickly understand a location in aparticular building (e.g., a residence) to which the devices areconnected and the patterns of energy usage that occur on the branchcircuits.

In one example, the series of dots 1606 on the “Kitchen” branch circuitmay correspond to a coffee maker turning on and off regularly to keepthe coffee temperature even. In another example, the series of dots 1608may correspond to an HVAC unit in the cellar which turns on/off atdifferent times and at different rates.

Based on the advantageous collection, processing, collation andpresentation of the power measurement and branch circuit identificationdata described herein, the system 200 acts as a pre-smoke detectorbecause it can detect potential fire hazards like sparks, failingappliances, abnormal power usage, and the like at a very earlystage—before the threat of actual fire even exists. The system's 200advanced detection and data processing techniques provide timely andimportant information and alerts to the homeowner, so that he or she canbe aware of dangerous conditions almost as soon as such conditions arepresent in the home's electrical system and take immediate action toameliorate the danger.

Another advantageous aspect of the system and method described herein isthe ability to determine whether external events (e.g., events happeningupstream from the home in the utility distribution system) are impactingpower delivery to the home and thus, may be representative of issuessuch as transformer failure or a surge on the utility line. In thisembodiment, the system 200 can be installed in multiple homes thatconnect to the same utility distribution system. As power measurementsare taken by the installed systems at each home, external energy eventsthat are detected by the respective systems can be correlated (e.g., thesystems detect a similar current/voltage change at the same time, andwhich is not directly attributable to any devices or circuits inside thehome) and a notification or alert can be generated for the utilityoperator to investigate potential causes. In some embodiments, thecurrent/voltage change may have a signature that is indicative ofspecific equipment failure (e.g., a transformer) and the signature canbe provided to the utility operator to help pinpoint the equipmentresponsible for the power change. In order to identify utility issues,the system 200 can sample data at high rates (e.g., on the order of 100MHz) and the corresponding data is synchronized between homes using anaccurate and stable clock source so that external events can becorrelated. Events can be correlated between sensors measuring dI/dt orvia the antenna measuring dV/dt. Events may also be correlated withexternal environmental factors such as weather (temperature, winds, rainor lightning) or solar events to help diagnose reasons for failure.

The above-described techniques can be implemented in digital and/oranalog electronic circuitry, or in computer hardware, firmware,software, or in combinations of them. The implementation can be as acomputer program product, i.e., a computer program tangibly embodied ina machine-readable storage device, for execution by, or to control theoperation of, a data processing apparatus, e.g., a programmableprocessor, a computer, and/or multiple computers. A computer program canbe written in any form of computer or programming language, includingsource code, compiled code, interpreted code and/or machine code, andthe computer program can be deployed in any form, including as astand-alone program or as a subroutine, element, or other unit suitablefor use in a computing environment. A computer program can be deployedto be executed on one computer or on multiple computers at one or moresites.

Method steps can be performed by one or more processors executing acomputer program to perform functions of the technology by operating oninput data and/or generating output data. Method steps can also beperformed by, and an apparatus can be implemented as, special purposelogic circuitry, e.g., a FPGA (field programmable gate array), a FPAA(field-programmable analog array), a CPLD (complex programmable logicdevice), a PSoC (Programmable System-on-Chip), ASIP(application-specific instruction-set processor), or an ASIC(application-specific integrated circuit), or the like. Subroutines canrefer to portions of the stored computer program and/or the processor,and/or the special circuitry that implement one or more functions.

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital or analog computer.Generally, a processor receives instructions and data from a read-onlymemory or a random access memory or both. The essential elements of acomputer are a processor for executing instructions and one or morememory devices for storing instructions and/or data. Memory devices,such as a cache, can be used to temporarily store data. Memory devicescan also be used for long-term data storage. Generally, a computer alsoincludes, or is operatively coupled to receive data from or transferdata to, or both, one or more mass storage devices for storing data,e.g., magnetic, magneto-optical disks, or optical disks. A computer canalso be operatively coupled to a communications network in order toreceive instructions and/or data from the network and/or to transferinstructions and/or data to the network. Computer-readable storagemediums suitable for embodying computer program instructions and datainclude all forms of volatile and non-volatile memory, including by wayof example semiconductor memory devices, e.g., DRAM, SRAM, EPROM,EEPROM, and flash memory devices; magnetic disks, e.g., internal harddisks or removable disks; magneto-optical disks; and optical disks,e.g., CD, DVD, HD-DVD, and Blu-ray disks. The processor and the memorycan be supplemented by and/or incorporated in special purpose logiccircuitry.

To provide for interaction with a user, the above described techniquescan be implemented on a computer in communication with a display device,e.g., a CRT (cathode ray tube), plasma, or LCD (liquid crystal display)monitor, for displaying information to the user and a keyboard and apointing device, e.g., a mouse, a trackball, a touchpad, or a motionsensor, by which the user can provide input to the computer (e.g.,interact with a user interface element). Other kinds of devices can beused to provide for interaction with a user as well; for example,feedback provided to the user can be any form of sensory feedback, e.g.,visual feedback, auditory feedback, or tactile feedback; and input fromthe user can be received in any form, including acoustic, speech, and/ortactile input.

The above described techniques can be implemented in a distributedcomputing system that includes a back-end component. The back-endcomponent can, for example, be a data server, a middleware component,and/or an application server. The above described techniques can beimplemented in a distributed computing system that includes a front-endcomponent. The front-end component can, for example, be a clientcomputer having a graphical user interface, a Web browser through whicha user can interact with an example implementation, and/or othergraphical user interfaces for a transmitting device. The above describedtechniques can be implemented in a distributed computing system thatincludes any combination of such back-end, middleware, or front-endcomponents.

The components of the computing system can be interconnected bytransmission medium, which can include any form or medium of digital oranalog data communication (e.g., a communication network). Transmissionmedium can include one or more packet-based networks and/or one or morecircuit-based networks in any configuration. Packet-based networks caninclude, for example, the Internet, a carrier internet protocol (IP)network (e.g., local area network (LAN), wide area network (WAN), campusarea network (CAN), metropolitan area network (MAN), home area network(HAN)), a private IP network, an IP private branch exchange (IPBX), awireless network (e.g., radio access network (RAN), Bluetooth, Wi-Fi,WiMAX, general packet radio service (GPRS) network, HiperLAN), and/orother packet-based networks. Circuit-based networks can include, forexample, the public switched telephone network (PSTN), a legacy privatebranch exchange (PBX), a wireless network (e.g., RAN, code-divisionmultiple access (CDMA) network, time division multiple access (TDMA)network, global system for mobile communications (GSM) network), and/orother circuit-based networks.

Information transfer over transmission medium can be based on one ormore communication protocols. Communication protocols can include, forexample, Ethernet protocol, Internet Protocol (IP), Voice over IP(VOIP), a Peer-to-Peer (P2P) protocol, Hypertext Transfer Protocol(HTTP), Session Initiation Protocol (SIP), H.323, Media Gateway ControlProtocol (MGCP), Signaling System #7 (SS7), a Global System for MobileCommunications (GSM) protocol, a Push-to-Talk (PTT) protocol, a PTT overCellular (POC) protocol, and/or other communication protocols.

Devices of the computing system can include, for example, a computer, acomputer with a browser device, a telephone, an IP phone, a mobiledevice (e.g., cellular phone, personal digital assistant (PDA) device,laptop computer, electronic mail device), and/or other communicationdevices. The browser device includes, for example, a computer (e.g.,desktop computer, laptop computer) with a World Wide Web browser (e.g.,Microsoft® Internet Explorer® available from Microsoft Corporation,Mozilla® Firefox available from Mozilla Corporation). Mobile computingdevice include, for example, a Blackberry®. IP phones include, forexample, a Cisco® Unified IP Phone 7985G available from Cisco Systems,Inc, and/or a Cisco® Unified Wireless Phone 7920 available from CiscoSystems, Inc.

Comprise, include, and/or plural forms of each are open ended andinclude the listed parts and can include additional parts that are notlisted. And/or is open ended and includes one or more of the listedparts and combinations of the listed parts.

One skilled in the art will realize the invention may be embodied inother specific forms without departing from the spirit or essentialcharacteristics thereof. The foregoing embodiments are therefore to beconsidered in all respects illustrative rather than limiting of theinvention described herein.

What is claimed is:
 1. A method for remote sensing to derive acalibrated power measurement for a power distribution point, the methodcomprising: sensing, by one or more magnetic field sensors of a sensormodule, a magnetic field emitted by the power distribution point,wherein the sensor module is positioned in proximity to the powerdistribution point; sensing, by a voltage sensor coupled to the sensormodule, a voltage carried in the power distribution point; generating,by a first processor coupled to the sensor module, an uncalibrated powermeasurement for each magnetic field sensor, the uncalibrated powermeasurement derived from the magnetic field sensed by the magnetic fieldsensor and the voltage sensed by the voltage sensor; determining, by asecond processor coupled to the sensor module, a response of eachmagnetic field sensor to a known power load being drawn through thepower distribution point; deriving, by the second processor, a transferfunction using the response of each magnetic field sensor to the knownpower load; and applying, by the second processor, the transfer functionto the uncalibrated power measurement for each magnetic field sensor togenerate the calibrated power measurement for the power distributionpoint.
 2. The method of claim 1, wherein the power distribution point isan electrical breaker box.
 3. The method of claim 2, wherein the sensormodule is affixed to the exterior of the electrical breaker box.
 4. Themethod of claim 1, wherein the voltage sensor is coupled to a powersupply electrically connected to the power distribution point.
 5. Themethod of claim 1, further comprising activating a calibrator circuitcoupled to the power distribution point to draw the known power loadthrough the power distribution point.
 6. The method of claim 1, whereinderiving a transfer function comprises deriving a series of coefficientseach associated with the uncalibrated power measurement for a magneticfield sensor.
 7. The method of claim 6, wherein deriving the series ofcoefficients comprises determining a quasi-power for each of themagnetic field sensors based upon the uncalibrated power measurement fora magnetic field sensor.
 8. The method of claim 7, wherein determiningthe quasi-power comprises: integrating for each magnetic field sensorthe uncalibrated power measurement in time to determine a current (I)measured by the magnetic field sensor; multiplying the current by avoltage from the voltage sensor (I·V); and integrating I·V over onecycle.
 9. The method of claim 1, wherein the second processor is locatedin a server computing device coupled to the sensor module.
 10. Themethod of claim 9, further comprising transmitting, by the firstprocessor, the uncalibrated power measurement for each magnetic fieldsensor to the server computing device.
 11. The method of claim 1,further comprising transmitting the calibrated power measurement for thepower distribution point to a server computing device.
 12. The method ofclaim 1, wherein the magnetic field sensors are arranged in apredetermined geometric configuration within a housing of the sensormodule.
 13. The method of claim 1, wherein at least a subset of themagnetic field sensors are arranged at different orientations withrespect to each other.
 14. The method of claim 1, further comprising:collecting, by a server computing device, a set of calibrated powermeasurements for each of a plurality of power distribution pointsdistributed across a predefined geographical area; and determining, bythe server computing device, one or more power signatures that arecommon across at least a plurality of the sets of calibrated powermeasurements.
 15. The method of claim 14, wherein the one or more powersignatures correspond to a failure of equipment in a power generationsystem that is coupled to the plurality of power distribution points.16. A system for remote sensing to derive a calibrated power measurementfor a power distribution point, the system comprising: a sensor modulewith one or more magnetic field sensors positioned in proximity to thepower distribution point, the magnetic field sensors configured to sensea magnetic field emitted by the power distribution point; a voltagesensor coupled to the sensor module, the voltage sensor configured tosense a voltage carried in the power distribution point; a firstprocessor coupled to the sensor module, the first processor configuredto generate an uncalibrated power measurement for each magnetic fieldsensor, the uncalibrated power measurement derived from the magneticfield sensed by the magnetic field sensor and the voltage sensed by thevoltage sensor; a second processor coupled to the sensor module, thesecond processor configured to: determine a response of each magneticfield sensor to a known power load being drawn through the powerdistribution point; derive a transfer function using the response ofeach magnetic field sensor to the known power load; and apply thetransfer function to the uncalibrated power measurement for eachmagnetic field sensor to generate the calibrated power measurement forthe power distribution point.
 17. The system of claim 16, wherein thepower distribution point is an electrical breaker box.
 18. The system ofclaim 17, wherein the sensor module is affixed to the exterior of theelectrical breaker box.
 19. The system of claim 16, wherein the voltagesensor is coupled to a power supply electrically connected to the powerdistribution point.
 20. The system of claim 16, further comprising acalibrator circuit coupled to the power distribution point that, whenactivated, draws the known power load through the power distributionpoint.
 21. The system of claim 16, wherein deriving a transfer functioncomprises deriving a series of coefficients each associated with theuncalibrated power measurement for a magnetic field sensor.
 22. Thesystem of claim 21, wherein deriving the series of coefficientscomprises determining a quasi-power for each of the magnetic fieldsensors based upon the uncalibrated power measurement for a magneticfield sensor.
 23. The system of claim 22, wherein determining thequasi-power comprises: integrating for each magnetic field sensor theuncalibrated power measurement in time to determine a current (I)measured by the magnetic field sensor; multiplying the current by avoltage from the voltage sensor (I·V); and integrating I·V over onecycle.
 24. The system of claim 16, wherein the second processor islocated in a server computing device coupled to the sensor module. 25.The system of claim 24, wherein the first processor is configured totransmit the uncalibrated power measurement for each magnetic fieldsensor to the server computing device.
 26. The system of claim 16,wherein the second processor is configured to transmit the calibratedpower measurement for the power distribution point to a server computingdevice.
 27. The system of claim 16, wherein the magnetic field sensorsare arranged in a predetermined geometric configuration within a housingof the sensor module.
 28. The system of claim 16, wherein at least asubset of the magnetic field sensors are arranged at differentorientations with respect to each other.
 29. The system of claim 16,further comprising a server computing device configured to: collect aset of calibrated power measurements for each of a plurality of powerdistribution points distributed across a predefined geographical area;and determine one or more power signatures that are common across atleast a plurality of the sets of calibrated power measurements.
 30. Thesystem of claim 29, wherein the one or more power signatures correspondto a failure of equipment in a power generation system that is coupledto the plurality of power distribution points.